Ultrafast neuromorphic computing driven by polariton nonlinearities

Yusong Gan; Ying Shi; Sanjib Ghosh; Haiyun Liu; Huawen Xu; Qihua Xiong

doi:10.1186/s43593-025-00087-9

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Ultrafast neuromorphic computing driven by polariton nonlinearities

eLight 2025年5卷

Research Article | Updated：2025-07-29

- Ultrafast neuromorphic computing driven by polariton nonlinearities
- Ultrafast neuromorphic computing driven by polariton nonlinearities
- Affiliations：
- Author bio：
- Funds：
  
  National Natural Science Foundation of China
- DOI：10.1186/s43593-025-00087-9
  中图分类号：
- 收稿日期：2025-01-22，
  
  修回日期：2025-03-19，
  
  录用日期：2025-04-01，
  
  网络出版日期：2025-06-02，
  
  纸质出版日期：2025-12
- Accepted：
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Ultrafast neuromorphic computing driven by polariton nonlinearities[J]. eLight, 2025,5.

Yusong Gan, Ying Shi, Sanjib Ghosh, et al. Ultrafast neuromorphic computing driven by polariton nonlinearities[J]. Elight, 2025, 5.
Ultrafast neuromorphic computing driven by polariton nonlinearities[J]. eLight, 2025,5. DOI： 10.1186/s43593-025-00087-9.

Yusong Gan, Ying Shi, Sanjib Ghosh, et al. Ultrafast neuromorphic computing driven by polariton nonlinearities[J]. Elight, 2025, 5. DOI： 10.1186/s43593-025-00087-9.

摘要

Abstract

Neuromorphic computing offers a promising approach to artificial intelligence by mimicking biological neural networks to perform complex tasks efficiently. While software-based simulations have demonstrated the potential of neuromorphic architectures

a physical platform is crucial to fully realize its computational advantages. Herein

we present the first demonstration of perovskite microcavity exciton polaritons as a platform for reservoir computing-based artificial neural networks. By leveraging the nonlinear response properties of exciton polaritons

we developed a neuromorphic computing architecture capable of performing classification tasks with single-step training

eliminating the need for iterative algorithms like backpropagation. Applying this system to a handwritten digit recognition task

we achieve 92% classification accuracy at room temperature. Notably

we also show that the system is dynamically nonlinear

further enhancing the potential to improve classification efficiency and address more complex tasks. Our findings advocate the promising capabilities of perovskite exciton polaritons as energy-efficient

ultrafast response platforms for artificial intelligence

paving the way for next-generation computational technologies.

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references

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